MultiQC_miso_batch1_verysensitive: MultiQC Report

General Statistics

Showing ¹⁴¹/₁₄₁ rows and ¹⁶/₂₉ columns.

Sample Name	% Dups (raw)	% GC (raw)	Avg. length (raw)	Median length (raw)	M Seqs (raw)	% Fails (raw)	% Dups (processed)	% GC (processed)	Avg. length (processed)	Median length (processed)	M Seqs (processed)	% Fails (processed)	% Duplication	% > Q30	Mb Q30 bases	Reads After Filtering	GC content	% PF	% Adapter	% Aligned (Host)	% Aligned (Assem.)	N50 (Kbp)	N50 (Kbp)	Assembly Length (Mbp)	Assembly Length (Mbp)	Contigs	Bases	CDS	Organism
MEGAHIT-CONCOCT-group-0_0																							1.7Kbp		0.0Mbp	1	1676	1	Genus species
MEGAHIT-CONCOCT-group-0_1																							4.8Kbp		3.1Mbp	757	3134381	3120	Genus species
MEGAHIT-CONCOCT-group-0_2																							1.8Kbp		2.0Mbp	1168	2031062	2260	Genus species
MEGAHIT-CONCOCT-group-0_3																							14.8Kbp		0.0Mbp	2	16511	17	Genus species
MEGAHIT-CONCOCT-group-0_4																							1.3Kbp		0.2Mbp	115	158173	114	Genus species
MEGAHIT-CONCOCT-group-0_5																							1.5Kbp		6.6Mbp	4274	6634995	6743	Genus species
MEGAHIT-CONCOCT-group-0_6																							2.0Kbp		0.0Mbp	28	49085	18	Genus species
MEGAHIT-CONCOCT-group-0_7																							1.4Kbp		0.0Mbp	2	2404	3	Genus species
MEGAHIT-CONCOCT-group-0_8																							1.0Kbp		0.0Mbp	1	1034	3	Genus species
MEGAHIT-CONCOCT-group-0_9																							3.1Kbp		0.0Mbp
MEGAHIT-CONCOCT-group-0_10																							9.2Kbp		9.4Mbp
MEGAHIT-CONCOCT-group-0_11																							170.5Kbp		0.2Mbp	2	205308	255	Genus species
MEGAHIT-CONCOCT-group-0_12																							1.8Kbp		0.1Mbp
MEGAHIT-CONCOCT-group-0_13																							1.1Kbp		0.0Mbp	1	1090	1	Genus species
MEGAHIT-CONCOCT-group-0_14																							1.3Kbp		0.0Mbp	2	2448	3	Genus species
MEGAHIT-CONCOCT-group-0_15																							1.4Kbp		0.3Mbp	237	342090	404	Genus species
MEGAHIT-CONCOCT-group-0_16																							1.7Kbp		4.1Mbp	2425	4058678	4097	Genus species
MEGAHIT-CONCOCT-group-0_17																							4.5Kbp		0.1Mbp	18	57492	72	Genus species
MEGAHIT-CONCOCT-group-0_18																							1.9Kbp		1.8Mbp	1010	1827179	1883	Genus species
MEGAHIT-CONCOCT-group-0_19																							1.5Kbp		0.0Mbp	2	2862	3	Genus species
MEGAHIT-CONCOCT-group-0_20																							1.1Kbp		0.0Mbp	2	2124	2	Genus species
MEGAHIT-CONCOCT-group-0_21																							2.7Kbp		1.1Mbp
MEGAHIT-CONCOCT-group-0_22																							239.3Kbp		0.2Mbp	1	239287	302	Genus species
MEGAHIT-CONCOCT-group-0_24																							26.7Kbp		0.0Mbp	4	44342	66	Genus species
MEGAHIT-CONCOCT-group-0_25																							2.0Kbp		0.0Mbp	11	21918	17	Genus species
MEGAHIT-CONCOCT-group-0_26																							1.3Kbp		0.0Mbp	4	4840	7	Genus species
MEGAHIT-CONCOCT-group-0_27																							1.2Kbp		0.0Mbp	3	4415	2	Genus species
MEGAHIT-CONCOCT-group-0_28																							4.2Kbp		0.0Mbp
MEGAHIT-CONCOCT-group-0_29																							1.8Kbp		3.2Mbp	1799	3151920	3029	Genus species
MEGAHIT-CONCOCT-group-0_30																							9.2Kbp		0.1Mbp
MEGAHIT-CONCOCT-group-0_31																							1.0Kbp		0.0Mbp	1	1034		Genus species
MEGAHIT-CONCOCT-group-0_32																							25.5Kbp		2.9Mbp	174	2852917	2903	Genus species
MEGAHIT-CONCOCT-group-0_33																							1.7Kbp		0.0Mbp	12	23962	14	Genus species
MEGAHIT-CONCOCT-group-0_34																							2.3Kbp		0.0Mbp
MEGAHIT-CONCOCT-group-0_35																							1.3Kbp		0.0Mbp	7	9599	12	Genus species
MEGAHIT-CONCOCT-group-0_36																							2.9Kbp		0.0Mbp	3	5037	8	Genus species
MEGAHIT-CONCOCT-group-0_37																							1.4Kbp		0.2Mbp	127	180142	132	Genus species
MEGAHIT-CONCOCT-group-0_38																							1.6Kbp		0.0Mbp	1	1563	1	Genus species
MEGAHIT-CONCOCT-group-0_39																							7.7Kbp		4.7Mbp	666	4652472	4835	Genus species
MEGAHIT-CONCOCT-group-0_40																							1.4Kbp		0.0Mbp	4	5384	4	Genus species
MEGAHIT-CONCOCT-group-0_41																							1.3Kbp		0.0Mbp	17	24331	24	Genus species
MEGAHIT-CONCOCT-group-0_42																							1.1Kbp		0.0Mbp	1	1117		Genus species
MEGAHIT-CONCOCT-group-0_43																							30.4Kbp		0.0Mbp	1	30391	21	Genus species
MEGAHIT-CONCOCT-group-0_44																							14.8Kbp		7.3Mbp	813	7255548	6886	Genus species
MEGAHIT-CONCOCT-group-0_45																							1.2Kbp		0.0Mbp	5	6644	5	Genus species
MEGAHIT-CONCOCT-group-0_46																							14.6Kbp		2.5Mbp
MEGAHIT-CONCOCT-group-0_47																							1.4Kbp		0.0Mbp	3	4541	4	Genus species
MEGAHIT-CONCOCT-group-0_48																							5.3Kbp		3.9Mbp	804	3880011	3704	Genus species
MEGAHIT-CONCOCT-group-0_49																							2.2Kbp		0.0Mbp	1	2173		Genus species
MEGAHIT-CONCOCT-group-0_50																							1.3Kbp		0.0Mbp	1	1267	2	Genus species
MEGAHIT-CONCOCT-group-0_51																							1.8Kbp		0.0Mbp	9	16659	19	Genus species
MEGAHIT-CONCOCT-group-0_52																							1.3Kbp		0.0Mbp	2	2464	2	Genus species
MEGAHIT-CONCOCT-group-0_53																							1.9Kbp		0.0Mbp	14	26427	44	Genus species
MEGAHIT-CONCOCT-group-0_54																							2.6Kbp		1.9Mbp	814	1941730	2183	Genus species
MEGAHIT-CONCOCT-group-0_55																							136.4Kbp		4.9Mbp	74	4874494	4598	Genus species
MEGAHIT-CONCOCT-group-0_56																							2.7Kbp		0.0Mbp	17	36993	47	Genus species
MEGAHIT-CONCOCT-group-0_57																							1.4Kbp		0.1Mbp
MEGAHIT-CONCOCT-group-0_58																							1.2Kbp		0.0Mbp	7	10331	18	Genus species
MEGAHIT-CONCOCT-group-0_60																							3.3Kbp		0.0Mbp	4	9619	13	Genus species
MEGAHIT-CONCOCT-group-0_61																							1.8Kbp		2.1Mbp	1201	2121564	2158	Genus species
MEGAHIT-CONCOCT-group-0_62																							1.5Kbp		1.4Mbp	902	1375240	1437	Genus species
MEGAHIT-CONCOCT-group-0_63																							3.6Kbp		0.0Mbp	5	16029	10	Genus species
MEGAHIT-CONCOCT-group-0_64																							2.5Kbp		0.0Mbp	17	43845	12	Genus species
MEGAHIT-CONCOCT-group-0_65																							1.1Kbp		0.0Mbp	1	1126		Genus species
MEGAHIT-CONCOCT-group-0_66																							3.0Kbp		0.1Mbp
MEGAHIT-CONCOCT-group-0_67																							39.2Kbp		5.5Mbp	259	5467423	5283	Genus species
MEGAHIT-CONCOCT-group-0_68																							1.2Kbp		0.0Mbp	1	1162		Genus species
MEGAHIT-MaxBin2-group-0.002																							9.9Kbp		2.9Mbp
MEGAHIT-MaxBin2-group-0.003																							21.3Kbp		5.2Mbp	406	5167809	4752	Genus species
MEGAHIT-MaxBin2-group-0.004																							2.6Kbp		1.5Mbp	643	1525539	1342	Genus species
MEGAHIT-MaxBin2-group-0.005																							2.8Kbp		2.1Mbp	857	2055796	1985	Genus species
MEGAHIT-MaxBin2-group-0.006																							2.5Kbp		2.4Mbp	1064	2391141	2498	Genus species
MEGAHIT-MaxBin2-group-0.007																							28.9Kbp		0.6Mbp	37	590936	663	Genus species
MEGAHIT-MaxBin2-group-0.008																							1.7Kbp		2.2Mbp	1266	2151383	2217	Genus species
MEGAHIT-MaxBin2-group-0.009																							42.7Kbp		5.2Mbp	308	5169997	4946	Genus species
MEGAHIT-MaxBin2-group-0.010																							2.2Kbp		6.2Mbp	3015	6213497	6504	Genus species
MEGAHIT-MaxBin2-group-0.011																							136.4Kbp		4.9Mbp	110	4942137	4661	Genus species
MEGAHIT-MaxBin2-group-0.012																							8.4Kbp		2.2Mbp	447	2192949	2496	Genus species
MEGAHIT-MaxBin2-group-0.014																							1.5Kbp		3.2Mbp	2041	3190771	3338	Genus species
MEGAHIT-MaxBin2-group-0.015																							27.1Kbp		3.1Mbp	266	3080845	3146	Genus species
MEGAHIT-MaxBin2-group-0.016																							3.2Kbp		4.0Mbp	1532	3963653	4260	Genus species
MEGAHIT-MaxBin2-group-0.017																							3.1Kbp		7.3Mbp	2879	7343053	7152	Genus species
MEGAHIT-MaxBin2-group-0.018																							3.4Kbp		4.8Mbp	1788	4788021	4717	Genus species
MEGAHIT-MetaBAT2-group-0.1																							239.3Kbp		0.2Mbp	1	239287	302	Genus species
MEGAHIT-MetaBAT2-group-0.2																							14.6Kbp		0.5Mbp
MEGAHIT-MetaBAT2-group-0.3																							2.4Kbp		1.0Mbp	410	965548	1034	Genus species
MEGAHIT-MetaBAT2-group-0.4																							32.7Kbp		6.1Mbp	416	6127836	5948	Genus species
MEGAHIT-MetaBAT2-group-0.6																							3.9Kbp		2.6Mbp	726	2575429	2540	Genus species
MEGAHIT-MetaBAT2-group-0.7																							8.8Kbp		1.1Mbp	190	1117767	1296	Genus species
MEGAHIT-MetaBAT2-group-0.8																							4.3Kbp		7.2Mbp
MEGAHIT-MetaBAT2-group-0.9																							15.6Kbp		0.4Mbp
MEGAHIT-MetaBAT2-group-0.10																							3.7Kbp		2.7Mbp	830	2748250	2637	Genus species
MEGAHIT-MetaBAT2-group-0.11																							5.8Kbp		0.6Mbp
MEGAHIT-MetaBAT2-group-0.12																							21.4Kbp		5.4Mbp	374	5353146	5047	Genus species
MEGAHIT-MetaBAT2-group-0.13																							2.1Kbp		0.9Mbp	447	947955	1013	Genus species
MEGAHIT-MetaBAT2-group-0.14																							4.3Kbp		4.3Mbp	1117	4286792	4085	Genus species
MEGAHIT-MetaBAT2-group-0.16																							172.6Kbp		2.3Mbp	19	2261055	2160	Genus species
MEGAHIT-MetaBAT2-group-0.17																							20.1Kbp		2.8Mbp	237	2831914	2879	Genus species
MEGAHIT-MetaBAT2-group-0.18																							15.4Kbp		0.8Mbp
MEGAHIT-MetaBAT2-group-0.19																							57.5Kbp		0.2Mbp	12	210757	231	Genus species
MEGAHIT-MetaBAT2-group-0.20																							170.5Kbp		0.2Mbp	2	205308	255	Genus species
MEGAHIT-MetaBAT2-group-0.21																							136.4Kbp		2.2Mbp	24	2163733	2007	Genus species
MEGAHIT-MetaBAT2-group-0.22																							11.1Kbp		0.5Mbp	112	517680	517	Genus species
MEGAHIT-MetaBAT2-group-0.23																							2.7Kbp		1.9Mbp	699	1853137	1951	Genus species
MEGAHIT-group-0																						8.6Kbp		124.4Mbp
MEGAHIT-group-0-T1																					98.6%
MEGAHIT-group-0-T2																					98.2%
MEGAHIT-group-0-T3																					99.1%
MEGAHIT-group-0-T4																					97.7%
MEGAHIT-group-0-T6																					98.5%
MEGAHIT-group-0-T8																					97.0%
T1_run0													0.3%	79.9%	963.8Mb	8.8M	47.0%	89.8%	25.2%	12.7%
T1_run0_raw_1	11.3%	47.0%	151bp	151bp	4.9M	18%
T1_run0_raw_2	11.3%	47.0%	151bp	151bp	4.9M	18%
T1_run0_trimmed_1							11.1%	47.0%	141bp	151bp	3.8M	9%
T1_run0_trimmed_2							11.3%	46.0%	141bp	151bp	3.8M	9%
T2_run0													0.3%	80.7%	505.0Mb	4.6M	46.4%	89.8%	29.7%	24.4%
T2_run0_raw_1	8.6%	47.0%	151bp	151bp	2.6M	18%
T2_run0_raw_2	8.0%	46.0%	151bp	151bp	2.6M	18%
T2_run0_trimmed_1							7.6%	46.0%	143bp	151bp	1.8M	9%
T2_run0_trimmed_2							7.2%	46.0%	143bp	151bp	1.8M	9%
T3_run0													0.5%	79.2%	2373.3Mb	21.3M	47.4%	88.6%	20.9%	1.7%
T3_run0_raw_1	18.7%	48.0%	151bp	151bp	12.0M	18%
T3_run0_raw_2	20.3%	47.0%	151bp	151bp	12.0M	18%
T3_run0_trimmed_1							18.4%	47.0%	141bp	151bp	10.5M	9%
T3_run0_trimmed_2							21.0%	47.0%	141bp	151bp	10.5M	9%
T4_run0													0.3%	79.8%	890.6Mb	8.0M	45.6%	87.7%	24.4%	13.2%
T4_run0_raw_1	9.5%	46.0%	151bp	151bp	4.6M	18%
T4_run0_raw_2	8.8%	45.0%	151bp	151bp	4.6M	18%
T4_run0_trimmed_1							9.0%	45.0%	142bp	151bp	3.5M	9%
T4_run0_trimmed_2							8.8%	44.0%	142bp	151bp	3.5M	9%
T6_run0													0.2%	79.2%	555.4Mb	5.1M	47.0%	87.5%	27.7%	17.3%
T6_run0_raw_1	8.7%	48.0%	151bp	151bp	2.9M	18%
T6_run0_raw_2	7.7%	47.0%	151bp	151bp	2.9M	9%
T6_run0_trimmed_1							8.4%	47.0%	142bp	151bp	2.1M	9%
T6_run0_trimmed_2							7.7%	46.0%	142bp	151bp	2.1M	9%
T8_run0													0.9%	80.6%	2651.6Mb	23.1M	40.5%	90.5%	18.8%	1.6%
T8_run0_raw_1	31.6%	41.0%	151bp	151bp	12.7M	27%
T8_run0_raw_2	31.4%	40.0%	151bp	151bp	12.7M	27%
T8_run0_trimmed_1							31.9%	40.0%	143bp	151bp	11.4M	18%
T8_run0_trimmed_2							33.4%	40.0%	143bp	151bp	11.4M	18%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	FastQC: raw reads	% Dups (raw)	% duplicate reads	`fastqc_raw_reads-percent_duplicates`
\|\|	FastQC: raw reads	% GC (raw)	Average % GC content	`fastqc_raw_reads-percent_gc`
\|\|	FastQC: raw reads	Avg. length (raw)	Average read length	`fastqc_raw_reads-avg_sequence_length`
\|\|	FastQC: raw reads	Median length (raw)	Median read length	`fastqc_raw_reads-median_sequence_length`
\|\|	FastQC: raw reads	M Seqs (raw)	Total sequences (millions)	`fastqc_raw_reads-total_sequences`	read_count
\|\|	FastQC: raw reads	% Fails (raw)	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw_reads-percent_fails`
\|\|	FastQC: after preprocessing	% Dups (processed)	% duplicate reads	`fastqc_after_preprocessing-percent_duplicates`
\|\|	FastQC: after preprocessing	% GC (processed)	Average % GC content	`fastqc_after_preprocessing-percent_gc`
\|\|	FastQC: after preprocessing	Avg. length (processed)	Average read length	`fastqc_after_preprocessing-avg_sequence_length`
\|\|	FastQC: after preprocessing	Median length (processed)	Median read length	`fastqc_after_preprocessing-median_sequence_length`
\|\|	FastQC: after preprocessing	M Seqs (processed)	Total sequences (millions)	`fastqc_after_preprocessing-total_sequences`	read_count
\|\|	FastQC: after preprocessing	% Fails (processed)	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_after_preprocessing-percent_fails`
\|\|	fastp	% Duplication	Duplication rate before filtering	`fastp-pct_duplication`
\|\|	fastp	% > Q30	Percentage of reads > Q30 after filtering	`fastp-after_filtering_q30_rate`
\|\|	fastp	Mb Q30 bases	Bases > Q30 after filtering (millions)	`fastp-after_filtering_q30_bases`	base_count
\|\|	fastp	Reads After Filtering	Total reads after filtering (millions)	`fastp-filtering_result_passed_filter_reads`	read_count
\|\|	fastp	GC content	GC content after filtering	`fastp-after_filtering_gc_content`
\|\|	fastp	% PF	Percent reads passing filter	`fastp-pct_surviving`
\|\|	fastp	% Adapter	Percentage adapter-trimmed reads	`fastp-pct_adapter`
\|\|	Bowtie2: host removal	% Aligned (Host)	overall alignment rate	`bowtie2_host_removal-overall_alignment_rate`
\|\|	Bowtie2: assembly	% Aligned (Assem.)	overall alignment rate	`bowtie2_assembly-overall_alignment_rate`
\|\|	QUAST: assembly	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly (kilo base pairs)	`quast_assembly-N50`
\|\|	QUAST: bins	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly (kilo base pairs)	`quast_bins-N50`
\|\|	QUAST: assembly	Assembly Length (Mbp)	The total number of bases in the assembly (Mbp).	`quast_assembly-Total_length`
\|\|	QUAST: bins	Assembly Length (Mbp)	The total number of bases in the assembly (Mbp).	`quast_bins-Total_length`
\|\|	Prokka	Contigs	Number of contigs	`prokka-contigs`
\|\|	Prokka	Bases	Number of bases	`prokka-bases`
\|\|	Prokka	CDS	Number of CDS	`prokka-CDS`
\|\|	Prokka	Organism	Organism	`prokka-organism`

FastQC: raw reads

Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

All samples have sequences of a single length (151bp)

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

12 samples had less than 1% of reads made up of overrepresented sequences

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ¹/₁ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	6	62002	0.0780%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_raw_reads-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_raw_reads-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_raw_reads-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

fastp

All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

Filtered Reads

Filtering statistics of sampled reads.

Created with MultiQC

Insert Sizes

Insert size estimation of sampled reads.

Created with MultiQC

Sequence Quality

Average sequencing quality over each base of all reads.

Created with MultiQC

GC Content

Average GC content over each base of all reads.

Created with MultiQC

N content

Average N content over each base of all reads.

Created with MultiQC

FastQC: after preprocessing

After trimming and, if requested, contamination removal.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

12 samples had less than 1% of reads made up of overrepresented sequences

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

Bowtie2: host removal

Mapping statistics of reads mapped against host genome and subsequently removed.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

Paired-end alignments

This plot shows the number of reads aligning to the reference in different ways.

Please note that single mate alignment counts are halved to tally with pair counts properly.

There are 6 possible types of alignment:

PE mapped uniquely: Pair has only one occurence in the reference genome.
PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
PE one mate mapped uniquely: One read of a pair has one occurence.
PE multimapped: Pair has multiple occurence.
PE one mate multimapped: One read of a pair has multiple occurence.
PE neither mate aligned: Pair has no occurence.

Created with MultiQC

QUAST: assembly

Assembly statistics of raw assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

Assembly Statistics

Showing ¹/₁ rows and ⁶/₆ columns.

Sample Name	N50 (Kbp)	N75 (Kbp)	L50 (K)	L75 (K)	Largest contig (Kbp)	Length (Mbp)
MEGAHIT-group-0	8.6Kbp	1.7Kbp	2.0K	11.3K	502.7Kbp	124.4Mbp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: quast_table_table

Sort	Column	Description	ID
\|\|	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly.	`quast-N50`
\|\|	N75 (Kbp)	N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly	`quast-N75`
\|\|	L50 (K)	L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length.	`quast-L50`
\|\|	L75 (K)	L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length.	`quast-L75`
\|\|	Largest contig (Kbp)	The size of the largest contig of the assembly	`quast-Largest_contig`
\|\|	Length (Mbp)	The total number of bases in the assembly.	`quast-Total_length`

Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

Created with MultiQC

Bowtie2: assembly

Mapping statistics of reads mapped against assemblies.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

Paired-end alignments

This plot shows the number of reads aligning to the reference in different ways.

Please note that single mate alignment counts are halved to tally with pair counts properly.

There are 6 possible types of alignment:

PE mapped uniquely: Pair has only one occurence in the reference genome.
PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
PE one mate mapped uniquely: One read of a pair has one occurence.
PE multimapped: Pair has multiple occurence.
PE one mate multimapped: One read of a pair has multiple occurence.
PE neither mate aligned: Pair has no occurence.

Created with MultiQC

QUAST: bins

Assembly statistics of binned assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

Assembly Statistics

Showing ¹⁰⁴/₁₀₄ rows and ⁶/₆ columns.

Sample Name	N50 (Kbp)	N75 (Kbp)	L50 (K)	L75 (K)	Largest contig (Kbp)	Length (Mbp)
MEGAHIT-CONCOCT-group-0_0	1.7Kbp	1.7Kbp	0.0K	0.0K	1.7Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_1	4.8Kbp	3.2Kbp	0.2K	0.4K	17.9Kbp	3.1Mbp
MEGAHIT-CONCOCT-group-0_2	1.8Kbp	1.3Kbp	0.4K	0.7K	6.1Kbp	2.0Mbp
MEGAHIT-CONCOCT-group-0_3	14.8Kbp	14.8Kbp	0.0K	0.0K	14.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_4	1.3Kbp	1.1Kbp	0.0K	0.1K	6.1Kbp	0.2Mbp
MEGAHIT-CONCOCT-group-0_5	1.5Kbp	1.2Kbp	1.5K	2.8K	5.8Kbp	6.6Mbp
MEGAHIT-CONCOCT-group-0_6	2.0Kbp	1.3Kbp	0.0K	0.0K	4.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_7	1.4Kbp	1.0Kbp	0.0K	0.0K	1.4Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_8	1.0Kbp	1.0Kbp	0.0K	0.0K	1.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_9	3.1Kbp	3.1Kbp	0.0K	0.0K	3.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_10	9.2Kbp	5.3Kbp	0.3K	0.6K	52.2Kbp	9.4Mbp
MEGAHIT-CONCOCT-group-0_11	170.5Kbp	170.5Kbp	0.0K	0.0K	170.5Kbp	0.2Mbp
MEGAHIT-CONCOCT-group-0_12	1.8Kbp	1.2Kbp	0.0K	0.0K	7.1Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_13	1.1Kbp	1.1Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_14	1.3Kbp	1.1Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_15	1.4Kbp	1.2Kbp	0.1K	0.2K	3.5Kbp	0.3Mbp
MEGAHIT-CONCOCT-group-0_16	1.7Kbp	1.3Kbp	0.9K	1.5K	6.4Kbp	4.1Mbp
MEGAHIT-CONCOCT-group-0_17	4.5Kbp	2.1Kbp	0.0K	0.0K	11.7Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_18	1.9Kbp	1.4Kbp	0.3K	0.6K	10.4Kbp	1.8Mbp
MEGAHIT-CONCOCT-group-0_19	1.5Kbp	1.4Kbp	0.0K	0.0K	1.5Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_20	1.1Kbp	1.0Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_21	2.7Kbp	1.7Kbp	0.1K	0.3K	29.7Kbp	1.1Mbp
MEGAHIT-CONCOCT-group-0_22	239.3Kbp	239.3Kbp	0.0K	0.0K	239.3Kbp	0.2Mbp
MEGAHIT-CONCOCT-group-0_24	26.7Kbp	9.9Kbp	0.0K	0.0K	26.7Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_25	2.0Kbp	1.4Kbp	0.0K	0.0K	5.2Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_26	1.3Kbp	1.1Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_27	1.2Kbp	1.1Kbp	0.0K	0.0K	2.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_28	4.2Kbp	1.5Kbp	0.0K	0.0K	5.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_29	1.8Kbp	1.4Kbp	0.6K	1.1K	6.1Kbp	3.2Mbp
MEGAHIT-CONCOCT-group-0_30	9.2Kbp	2.4Kbp	0.0K	0.0K	26.2Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_31	1.0Kbp	1.0Kbp	0.0K	0.0K	1.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_32	25.5Kbp	12.5Kbp	0.0K	0.1K	100.8Kbp	2.9Mbp
MEGAHIT-CONCOCT-group-0_33	1.7Kbp	1.3Kbp	0.0K	0.0K	8.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_34	2.3Kbp	1.8Kbp	0.0K	0.0K	4.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_35	1.3Kbp	1.2Kbp	0.0K	0.0K	1.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_36	2.9Kbp	1.1Kbp	0.0K	0.0K	2.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_37	1.4Kbp	1.2Kbp	0.1K	0.1K	7.6Kbp	0.2Mbp
MEGAHIT-CONCOCT-group-0_38	1.6Kbp	1.6Kbp	0.0K	0.0K	1.6Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_39	7.7Kbp	5.5Kbp	0.2K	0.4K	50.6Kbp	4.7Mbp
MEGAHIT-CONCOCT-group-0_40	1.4Kbp	1.1Kbp	0.0K	0.0K	1.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_41	1.3Kbp	1.1Kbp	0.0K	0.0K	3.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_42	1.1Kbp	1.1Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_43	30.4Kbp	30.4Kbp	0.0K	0.0K	30.4Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_44	14.8Kbp	5.8Kbp	0.1K	0.3K	194.9Kbp	7.3Mbp
MEGAHIT-CONCOCT-group-0_45	1.2Kbp	1.1Kbp	0.0K	0.0K	2.2Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_46	14.6Kbp	6.0Kbp	0.1K	0.1K	59.2Kbp	2.5Mbp
MEGAHIT-CONCOCT-group-0_47	1.4Kbp	1.3Kbp	0.0K	0.0K	1.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_48	5.3Kbp	3.6Kbp	0.2K	0.5K	25.2Kbp	3.9Mbp
MEGAHIT-CONCOCT-group-0_49	2.2Kbp	2.2Kbp	0.0K	0.0K	2.2Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_50	1.3Kbp	1.3Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_51	1.8Kbp	1.3Kbp	0.0K	0.0K	3.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_52	1.3Kbp	1.1Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_53	1.9Kbp	1.4Kbp	0.0K	0.0K	3.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_54	2.6Kbp	1.7Kbp	0.2K	0.4K	27.1Kbp	1.9Mbp
MEGAHIT-CONCOCT-group-0_55	136.4Kbp	78.4Kbp	0.0K	0.0K	437.3Kbp	4.9Mbp
MEGAHIT-CONCOCT-group-0_56	2.7Kbp	1.3Kbp	0.0K	0.0K	7.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_57	1.4Kbp	1.1Kbp	0.0K	0.0K	3.9Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_58	1.2Kbp	1.1Kbp	0.0K	0.0K	2.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_60	3.3Kbp	1.5Kbp	0.0K	0.0K	3.7Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_61	1.8Kbp	1.4Kbp	0.4K	0.7K	19.7Kbp	2.1Mbp
MEGAHIT-CONCOCT-group-0_62	1.5Kbp	1.2Kbp	0.3K	0.6K	5.8Kbp	1.4Mbp
MEGAHIT-CONCOCT-group-0_63	3.6Kbp	2.6Kbp	0.0K	0.0K	7.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_64	2.5Kbp	2.0Kbp	0.0K	0.0K	10.6Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_65	1.1Kbp	1.1Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_66	3.0Kbp	2.3Kbp	0.0K	0.0K	13.5Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_67	39.2Kbp	18.1Kbp	0.0K	0.1K	129.7Kbp	5.5Mbp
MEGAHIT-CONCOCT-group-0_68	1.2Kbp	1.2Kbp	0.0K	0.0K	1.2Kbp	0.0Mbp
MEGAHIT-MaxBin2-group-0.002	9.9Kbp	3.0Kbp	0.1K	0.2K	59.2Kbp	2.9Mbp
MEGAHIT-MaxBin2-group-0.003	21.3Kbp	12.3Kbp	0.1K	0.1K	194.9Kbp	5.2Mbp
MEGAHIT-MaxBin2-group-0.004	2.6Kbp	1.6Kbp	0.2K	0.3K	29.8Kbp	1.5Mbp
MEGAHIT-MaxBin2-group-0.005	2.8Kbp	1.8Kbp	0.2K	0.5K	21.0Kbp	2.1Mbp
MEGAHIT-MaxBin2-group-0.006	2.5Kbp	1.6Kbp	0.3K	0.6K	42.0Kbp	2.4Mbp
MEGAHIT-MaxBin2-group-0.007	28.9Kbp	15.8Kbp	0.0K	0.0K	239.3Kbp	0.6Mbp
MEGAHIT-MaxBin2-group-0.008	1.7Kbp	1.3Kbp	0.4K	0.8K	6.5Kbp	2.2Mbp
MEGAHIT-MaxBin2-group-0.009	42.7Kbp	18.1Kbp	0.0K	0.1K	129.7Kbp	5.2Mbp
MEGAHIT-MaxBin2-group-0.010	2.2Kbp	1.4Kbp	0.8K	1.7K	16.6Kbp	6.2Mbp
MEGAHIT-MaxBin2-group-0.011	136.4Kbp	78.4Kbp	0.0K	0.0K	437.3Kbp	4.9Mbp
MEGAHIT-MaxBin2-group-0.012	8.4Kbp	4.0Kbp	0.1K	0.2K	57.5Kbp	2.2Mbp
MEGAHIT-MaxBin2-group-0.014	1.5Kbp	1.2Kbp	0.7K	1.3K	26.7Kbp	3.2Mbp
MEGAHIT-MaxBin2-group-0.015	27.1Kbp	11.5Kbp	0.0K	0.1K	170.5Kbp	3.1Mbp
MEGAHIT-MaxBin2-group-0.016	3.2Kbp	1.7Kbp	0.3K	0.8K	28.3Kbp	4.0Mbp
MEGAHIT-MaxBin2-group-0.017	3.1Kbp	1.7Kbp	0.6K	1.5K	25.2Kbp	7.3Mbp
MEGAHIT-MaxBin2-group-0.018	3.4Kbp	1.8Kbp	0.4K	0.9K	23.8Kbp	4.8Mbp
MEGAHIT-MetaBAT2-group-0.1	239.3Kbp	239.3Kbp	0.0K	0.0K	239.3Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.2	14.6Kbp	6.5Kbp	0.0K	0.0K	59.2Kbp	0.5Mbp
MEGAHIT-MetaBAT2-group-0.3	2.4Kbp	1.9Kbp	0.2K	0.3K	8.9Kbp	1.0Mbp
MEGAHIT-MetaBAT2-group-0.4	32.7Kbp	15.0Kbp	0.1K	0.1K	129.7Kbp	6.1Mbp
MEGAHIT-MetaBAT2-group-0.6	3.9Kbp	2.5Kbp	0.2K	0.4K	17.8Kbp	2.6Mbp
MEGAHIT-MetaBAT2-group-0.7	8.8Kbp	4.8Kbp	0.0K	0.1K	31.9Kbp	1.1Mbp
MEGAHIT-MetaBAT2-group-0.8	4.3Kbp	2.5Kbp	0.5K	1.1K	27.4Kbp	7.2Mbp
MEGAHIT-MetaBAT2-group-0.9	15.6Kbp	4.9Kbp	0.0K	0.0K	48.2Kbp	0.4Mbp
MEGAHIT-MetaBAT2-group-0.10	3.7Kbp	2.5Kbp	0.2K	0.5K	47.3Kbp	2.7Mbp
MEGAHIT-MetaBAT2-group-0.11	5.8Kbp	3.2Kbp	0.0K	0.1K	25.7Kbp	0.6Mbp
MEGAHIT-MetaBAT2-group-0.12	21.4Kbp	13.0Kbp	0.1K	0.1K	194.9Kbp	5.4Mbp
MEGAHIT-MetaBAT2-group-0.13	2.1Kbp	1.8Kbp	0.2K	0.3K	5.9Kbp	0.9Mbp
MEGAHIT-MetaBAT2-group-0.14	4.3Kbp	2.8Kbp	0.3K	0.6K	25.2Kbp	4.3Mbp
MEGAHIT-MetaBAT2-group-0.16	172.6Kbp	112.7Kbp	0.0K	0.0K	437.3Kbp	2.3Mbp
MEGAHIT-MetaBAT2-group-0.17	20.1Kbp	10.7Kbp	0.0K	0.1K	100.8Kbp	2.8Mbp
MEGAHIT-MetaBAT2-group-0.18	15.4Kbp	7.3Kbp	0.0K	0.0K	56.4Kbp	0.8Mbp
MEGAHIT-MetaBAT2-group-0.19	57.5Kbp	16.5Kbp	0.0K	0.0K	58.5Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.20	170.5Kbp	170.5Kbp	0.0K	0.0K	170.5Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.21	136.4Kbp	78.5Kbp	0.0K	0.0K	269.5Kbp	2.2Mbp
MEGAHIT-MetaBAT2-group-0.22	11.1Kbp	2.3Kbp	0.0K	0.0K	78.4Kbp	0.5Mbp
MEGAHIT-MetaBAT2-group-0.23	2.7Kbp	2.0Kbp	0.2K	0.4K	13.7Kbp	1.9Mbp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: quast_table-1_table

Sort	Column	Description	ID
\|\|	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly.	`quast-N50`
\|\|	N75 (Kbp)	N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly	`quast-N75`
\|\|	L50 (K)	L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length.	`quast-L50`
\|\|	L75 (K)	L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length.	`quast-L75`
\|\|	Largest contig (Kbp)	The size of the largest contig of the assembly	`quast-Largest_contig`
\|\|	Length (Mbp)	The total number of bases in the assembly.	`quast-Total_length`

Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

Created with MultiQC

CheckM

Estimates genome completeness and contamination based on the presence or absence of marker genes.URL: https://github.com/Ecogenomics/CheckMDOI: 10.1101/gr.186072.114

Bin quality

The quality of microbial genomes recovered from isolates, single cells, and metagenomes.

An automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes.

Showing ⁸⁸/₈₈ rows and ⁶/₆ columns.

Bin Id	Marker lineage	Genomes	Markers	Marker sets	Completeness	Contamination
MEGAHIT-CONCOCT-group-0_0	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_1	f__Paenibacillaceae (UID973)	32	468	170	43.62%	10.45%
MEGAHIT-CONCOCT-group-0_2	k__Bacteria (UID203)	5449	104	58	37.93%	1.72%
MEGAHIT-CONCOCT-group-0_3	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_4	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_5	f__Paenibacillaceae (UID971)	46	481	186	74.46%	53.57%
MEGAHIT-CONCOCT-group-0_6	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_7	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_8	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_11	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_13	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_14	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_15	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_16	f__Paenibacillaceae (UID973)	32	468	170	53.77%	18.65%
MEGAHIT-CONCOCT-group-0_17	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_18	k__Bacteria (UID203)	5449	101	56	41.56%	9.82%
MEGAHIT-CONCOCT-group-0_19	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_20	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_22	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_24	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_25	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_26	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_27	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_29	k__Bacteria (UID203)	5449	104	58	87.93%	55.52%
MEGAHIT-CONCOCT-group-0_31	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_32	k__Bacteria (UID203)	5449	104	58	96.55%	9.56%
MEGAHIT-CONCOCT-group-0_33	k__Bacteria (UID203)	5449	101	56	2.27%	0.00%
MEGAHIT-CONCOCT-group-0_35	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_36	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_37	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_38	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_39	k__Bacteria (UID203)	5449	103	57	47.37%	2.63%
MEGAHIT-CONCOCT-group-0_40	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_41	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_42	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_43	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_44	k__Bacteria (UID203)	5449	104	58	89.18%	134.45%
MEGAHIT-CONCOCT-group-0_45	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_47	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_48	k__Bacteria (UID203)	5449	104	58	49.11%	0.00%
MEGAHIT-CONCOCT-group-0_49	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_50	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_51	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_52	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_53	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_54	f__Bacillaceae (UID829)	128	559	183	5.40%	0.13%
MEGAHIT-CONCOCT-group-0_55	f__Enterobacteriaceae (UID5124)	134	1173	336	99.07%	0.65%
MEGAHIT-CONCOCT-group-0_56	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_58	o__Bacillales (UID828)	139	508	174	0.36%	0.08%
MEGAHIT-CONCOCT-group-0_60	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_61	o__Bacillales (UID828)	139	508	174	60.50%	1.27%
MEGAHIT-CONCOCT-group-0_62	k__Bacteria (UID203)	5449	103	57	32.43%	17.37%
MEGAHIT-CONCOCT-group-0_63	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_64	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_65	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_67	f__Bacillaceae (UID829)	128	559	183	91.43%	2.74%
MEGAHIT-CONCOCT-group-0_68	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MaxBin2-group-0.003	c__Bacilli (UID285)	586	325	181	94.11%	73.40%
MEGAHIT-MaxBin2-group-0.004	k__Bacteria (UID203)	5449	104	58	88.06%	61.44%
MEGAHIT-MaxBin2-group-0.005	k__Bacteria (UID203)	5449	104	58	33.97%	6.90%
MEGAHIT-MaxBin2-group-0.006	k__Bacteria (UID203)	5449	104	58	19.83%	2.59%
MEGAHIT-MaxBin2-group-0.007	root (UID1)	5656	56	24	4.17%	0.00%
MEGAHIT-MaxBin2-group-0.008	k__Bacteria (UID203)	5449	104	58	62.30%	16.07%
MEGAHIT-MaxBin2-group-0.009	f__Bacillaceae (UID829)	128	559	183	87.50%	2.59%
MEGAHIT-MaxBin2-group-0.010	f__Paenibacillaceae (UID973)	32	468	170	78.73%	45.75%
MEGAHIT-MaxBin2-group-0.011	f__Enterobacteriaceae (UID5124)	134	1173	336	99.07%	0.69%
MEGAHIT-MaxBin2-group-0.012	k__Bacteria (UID203)	5449	103	57	2.63%	0.00%
MEGAHIT-MaxBin2-group-0.014	g__Bacillus (UID865)	36	1200	269	64.83%	22.08%
MEGAHIT-MaxBin2-group-0.015	k__Bacteria (UID203)	5449	104	58	98.28%	34.78%
MEGAHIT-MaxBin2-group-0.016	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MaxBin2-group-0.017	k__Bacteria (UID203)	5449	104	58	90.36%	44.28%
MEGAHIT-MaxBin2-group-0.018	c__Bacilli (UID259)	750	275	151	70.49%	16.78%
MEGAHIT-MetaBAT2-group-0.1	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.3	k__Bacteria (UID203)	5449	103	57	19.30%	0.00%
MEGAHIT-MetaBAT2-group-0.4	f__Bacillaceae (UID829)	128	559	183	92.78%	3.59%
MEGAHIT-MetaBAT2-group-0.6	k__Bacteria (UID203)	5449	104	58	45.45%	0.00%
MEGAHIT-MetaBAT2-group-0.7	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.10	k__Bacteria (UID203)	5449	104	58	60.83%	20.45%
MEGAHIT-MetaBAT2-group-0.12	k__Bacteria (UID203)	5449	104	58	89.58%	89.47%
MEGAHIT-MetaBAT2-group-0.13	k__Bacteria (UID203)	5449	104	58	29.18%	1.72%
MEGAHIT-MetaBAT2-group-0.14	k__Bacteria (UID203)	5449	104	58	56.69%	1.72%
MEGAHIT-MetaBAT2-group-0.16	root (UID1)	5656	56	24	12.50%	0.00%
MEGAHIT-MetaBAT2-group-0.17	k__Bacteria (UID203)	5449	104	58	96.55%	19.67%
MEGAHIT-MetaBAT2-group-0.19	k__Bacteria (UID203)	5449	103	57	0.88%	0.00%
MEGAHIT-MetaBAT2-group-0.20	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.21	f__Enterobacteriaceae (UID5124)	134	1173	336	53.21%	0.06%
MEGAHIT-MetaBAT2-group-0.22	f__Enterobacteriaceae (UID5124)	134	1172	336	6.41%	0.09%
MEGAHIT-MetaBAT2-group-0.23	f__Paenibacillaceae (UID973)	32	468	170	39.13%	1.37%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: checkm-first-table_table

Sort	Column	Description	ID
\|\|	Marker lineage	indicates lineage used for inferring marker set (a precise indication of where a bin was placed in CheckM's reference tree can be obtained with the tree_qa command)	`Marker_lineage`
\|\|	Genomes	Number of reference genomes used to infer marker set.	`genomes`
\|\|	Markers	Number of inferred marker genes.	`markers`
\|\|	Marker sets	Number of inferred co-located marker sets	`marker_sets`
\|\|	Completeness	Estimated completeness of genome as determined from the presence/absence of marker genes and the expected collocalization of these genes	`Completeness`
\|\|	Contamination	Estimated contamination of genome as determined by the presence of multi-copy marker genes and the expected collocalization of these genes	`Contamination`

Prokka

Rapid annotation of prokaryotic genomes.URL: http://www.vicbioinformatics.com/software.prokka.shtmlDOI: 10.1093/bioinformatics/btu153

This barplot shows the distribution of different types of features found in each contig.

Prokka can detect different features:

CDS
rRNA
tmRNA
tRNA
miscRNA
signal peptides
CRISPR arrays

This barplot shows you the distribution of these different types of features found in each contig.

Created with MultiQC

GTDB-Tk

Assigns objective taxonomic classifications to bacterial and archaeal genomes.URL: https://ecogenomics.github.io/GTDBTk/index.htmlDOI: 10.1093/bioinformatics/btac672

MAG taxonomy

The taxonomy of a MAG as found by GTDB.

GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB. It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes.

Showing ¹⁶/₁₆ rows and ⁶/₈ columns.

User genome	Classification	Full classification	Classification method	ANI to closest genome	AF to closest genome	RED	Warnings	Notes
MEGAHIT-CONCOCT-group-0_2.fa	s__Paenibacillus cookii	d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus cookii	ani_screen	98.8	0.9			classification based on ANI only
MEGAHIT-CONCOCT-group-0_18.fa	s__	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_G; g__Bacillus_A; s__	taxonomic classification defined by topology and ANI			1.0
MEGAHIT-CONCOCT-group-0_32.fa	s__Kurthia gibsonii	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_A; f__Planococcaceae; g__Kurthia; s__Kurthia gibsonii	ani_screen	98.7	0.9			classification based on ANI only
MEGAHIT-CONCOCT-group-0_39.fa	s__	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_A; f__Planococcaceae; g__Lysinibacillus; s__	taxonomic classification defined by topology and ANI			1.0
MEGAHIT-CONCOCT-group-0_48.fa	s__Paenibacillus polymyxa	d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus polymyxa	ani_screen	98.0	0.8			classification based on ANI only
MEGAHIT-CONCOCT-group-0_55.fa	s__Enterobacter cloacae_M	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter cloacae_M	ani_screen	98.2	0.9			classification based on ANI only
MEGAHIT-CONCOCT-group-0_61.fa	s__Priestia flexa	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_H; g__Priestia; s__Priestia flexa	ani_screen	99.5	0.8			classification based on ANI only
MEGAHIT-CONCOCT-group-0_67.fa	s__Niallia circulans_A	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_B; f__DSM-18226; g__Niallia; s__Niallia circulans_A	ani_screen	99.5	0.9			classification based on ANI only
MEGAHIT-MaxBin2-group-0.005.fa	s__	d__Bacteria; p__Bacillota; c__Bacilli; o__Staphylococcales; f__Staphylococcaceae; g__Staphylococcus; s__	taxonomic classification defined by topology and ANI			1.0	Genome has more than 14.2% of markers with multiple hits
MEGAHIT-MaxBin2-group-0.009.fa	s__Niallia circulans_A	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_B; f__DSM-18226; g__Niallia; s__Niallia circulans_A	ani_screen	99.4	0.9			classification based on ANI only
MEGAHIT-MaxBin2-group-0.011.fa	s__Enterobacter cloacae_M	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter cloacae_M	ani_screen	98.2	0.9			classification based on ANI only
MEGAHIT-MetaBAT2-group-0.4.fa	s__Niallia circulans_A	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_B; f__DSM-18226; g__Niallia; s__Niallia circulans_A	ani_screen	99.4	0.9			classification based on ANI only
MEGAHIT-MetaBAT2-group-0.6.fa	s__	d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__	taxonomic classification defined by topology and ANI			1.0
MEGAHIT-MetaBAT2-group-0.14.fa	s__Paenibacillus polymyxa	d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus polymyxa	ani_screen	98.1	0.7			classification based on ANI only
MEGAHIT-MetaBAT2-group-0.21.fa	s__Enterobacter cloacae_M	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter cloacae_M	ani_screen	98.5	0.9			classification based on ANI only
MEGAHIT-MetaBAT2-group-0.23.fa	s__Paenibacillus cookii	d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus cookii	ani_screen	98.7	0.9			classification based on ANI only

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: gtdbtk-first-table_table

Sort	Column	Description	ID
\|\|	Classification	GTDB taxonomy string inferred by the GTDB-Tk.	`classification`
\|\|	Full classification	Full GTDB taxonomy string inferred by the GTDB-Tk.	`full_classification`
\|\|	Classification method	Indicates the rule used to classify the genome.	`classification_method`
\|\|	ANI to closest genome	Depending on the classification method, either the 'closest_genome_ani' or 'closest_placement_ani'.	`ANI`
\|\|	AF to closest genome	Depending on the classification method, either the 'closest_genome_af' or 'closest_placement_af'.	`AF`
\|\|	RED	Indicates the relative evolutionary divergence (RED) for a query genome. RED is not calculated when a query genome can be classified based on ANI.	`red_value`
\|\|	Warnings	Indicates unusual characteristics of the query genome that may impact the taxonomic assignment.	`warnings`
\|\|	Notes	Provides additional information regarding the classification of the genome.	`note`

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
ADJUST_MAXBIN2_EXT	coreutils	`9.5`
BIN_SUMMARY	pandas	`1.4.3`
	python	`3.10.6`
BOWTIE2_ASSEMBLY_ALIGN	bowtie2	`2.4.2`
	pigz	`2.3.4`
	samtools	`1.11`
BOWTIE2_ASSEMBLY_BUILD	bowtie2	`2.4.2`
BOWTIE2_HOST_REMOVAL_ALIGN	bowtie2	`2.4.2`
BOWTIE2_HOST_REMOVAL_BUILD	bowtie2	`2.4.2`
CHECKM_LINEAGEWF	checkm	`1.2.3`
CHECKM_QA	checkm	`1.2.3`
CONCAT_BINQC_TSV	csvtk	`0.31.0`
CONCOCT_CONCOCT	concoct	`1.1.0`
CONCOCT_CONCOCTCOVERAGETABLE	concoct	`1.1.0`
CONCOCT_CUTUPFASTA	concoct	`1.1.0`
CONCOCT_EXTRACTFASTABINS	concoct	`1.1.0`
CONCOCT_MERGECUTUPCLUSTERING	concoct	`1.1.0`
CONVERT_DEPTHS	bioawk	`20110810`
DASTOOL_DASTOOL	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_CONCOCT	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_MAXBIN2	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_METABAT2	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_TIARA	dastool	`1.1.7`
FASTQC_RAW	fastqc	`0.12.1`
FASTQC_TRIMMED	fastqc	`0.12.1`
GENOMAD_ENDTOEND	genomad	`1.11.0`
GTDBTK_CLASSIFYWF	gtdb_db	`r226`
	gtdbtk	`2.5.2`
GTDBTK_SUMMARY	pandas	`1.4.3`
	python	`3.10.6`
GUNZIP_BINS	gunzip	`1.13`
GUNZIP_SHORTREAD_ASSEMBLIES	gunzip	`1.13`
GUNZIP_UNBINS	gunzip	`1.13`
MAG_DEPTHS	pandas	`1.1.5`
	python	`3.6.7`
MAG_DEPTHS_PLOT	pandas	`1.3.0`
	python	`3.9.6`
	seaborn	`0.11.0`
MAG_DEPTHS_SUMMARY	pandas	`1.4.3`
	python	`3.10.6`
MAXBIN2	maxbin2	`2.2.7`
MEGAHIT	megahit	`1.2.9`
METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS_SHORTREAD	metabat2	`2.15`
METABAT2_METABAT2	metabat2	`2.17`
PRODIGAL	pigz	`2.6`
	prodigal	`2.6.3`
Prokka	prokka	`1.14.6`
QUAST	metaquast	`5.0.2`
	python	`3.7.6`
QUAST_BINS	metaquast	`5.0.2`
	python	`3.7.6`
QUAST_BINS_SUMMARY	cp	`9.5`
RENAME_POSTDASTOOL	coreutils	`9.5`
RENAME_PREDASTOOL	coreutils	`9.5`
SEQKIT_STATS	seqkit	`2.9.0`
SPLIT_FASTA	biopython	`1.7.4`
	pandas	`1.1.5`
	python	`3.6.7`
TIARA_SUMMARY	csvtk	`0.31.0`
TIARA_TIARA	tiara	`1.0.3`
Workflow	Nextflow	`25.04.8`
	nf-core/mag	`v5.0.0-g3d41222`
fastp	fastp	`0.24.0`
r-base	r-base	`4.1.3`
r-tidyverse	r-tidyverse	`1.3.1`

nf-core/mag Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/mag

Methods

Data was processed using nf-core/mag v5.0.0 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v25.04.8 (Di Tommaso et al., 2017) with the following command:

nextflow run 'https://github.com/nf-core/mag' -name anna_miso_batch1_3 -params-file 'https://api.cloud.seqera.io/ephemeral/YTqQQMzoHmADjbz_X_QShw.json' -with-tower -r 3d41222776d2ed7a78dd3fd4dd643690c49f6bd2 -resume a4414311-4649-4eac-9943-0ba7e73b85f0

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/mag Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/mag

Input/output options

email: annalifousi@gmail.com
input: az://seqera/results/anna-mags/miso_batch1.csv
multiqc_title: MultiQC_miso_batch1_verysensitive
outdir: az://seqera/results/anna-mags/results_miso_new/

Quality control for short reads options

host_fasta: az://seqera/raw/miso/miso_genome/miso_genome.fna
host_removal_save_ids: true
host_removal_verysensitive: true
keep_phix: true
save_clipped_reads: true
save_hostremoved_reads: true

Quality control for long reads options

longreads_min_quality: 1

Taxonomic profiling options

cat_db: az://seqera/databases/cat/20241212_CAT_nr_website/
gtdb_db: az://seqera/databases/gtdb/GTDB/gtdbtk_package/full_package/release226/
gtdbtk_max_contamination: 10.0
gtdbtk_min_completeness: 30
gtdbtk_use_full_tree: true

Assembly options

coassemble_group: true
skip_flye: true
skip_metamdbg: true
skip_spades: true
skip_spadeshybrid: true

Gene prediction and annotation options

skip_metaeuk: true

Virus identification options

genomad_db: az://seqera/databases/geNomadDB/14886553/genomad_db/
run_virus_identification: true

Binning options

bin_domain_classification: true
bin_max_size: 10000000
exclude_unbins_from_postbinning: true
min_length_unbinned_contigs: 20000
save_assembly_mapped_reads: true

Bin quality check options

binqc_tool: checkm
busco_db_lineage: N/A
checkm_db: az://seqera/databases/checkm_data/
gunc_save_db: true
refine_bins_dastool: true

Core Nextflow options

configFiles: /.nextflow/assets/nf-core/mag/nextflow.config, /mnt/batch/tasks/workitems/nf-workflow-5VD8wN49lBdb1P/job-1/nf-workflow-5VD8wN49lBdb1P/wd/nextflow.config
launchDir: /mnt/batch/tasks/workitems/nf-workflow-5VD8wN49lBdb1P/job-1/nf-workflow-5VD8wN49lBdb1P/wd
profile: standard
projectDir: /.nextflow/assets/nf-core/mag
revision: 5.0.0
runName: anna_miso_batch1_3
userName: root
workDir: /seqera/results/anna-mags/work

Toggle navigation v1.29

MultiQC_miso_batch1_verysensitive

MultiQC Toolbox

Apply Highlight Samples

Apply Rename Samples

Apply Show / Hide Samples

Explain with AI

Export Plots

Choose Plots

Save Settings

Load Settings

Tool Citations

About MultiQC

MultiQC_miso_batch1_verysensitive

General Statistics

General Statistics: Columns

FastQC: raw reads

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences by sample Help

Top overrepresented sequences

FastQC: Top overrepresented sequences: Columns

Adapter Content Help

Status Checks Help

fastp

Filtered Reads

Insert Sizes

Sequence Quality

GC Content

N content

FastQC: after preprocessing

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences by sample Help

Adapter Content Help

Status Checks Help

Bowtie2: host removal

Paired-end alignments Help

QUAST: assembly

Assembly Statistics

QUAST: Assembly Statistics: Columns

Number of Contigs

Bowtie2: assembly

Paired-end alignments Help

QUAST: bins

Assembly Statistics

QUAST: Assembly Statistics: Columns

Number of Contigs

CheckM

Bin quality Help

Genome Quality: Columns

Prokka

Help

GTDB-Tk

MAG taxonomy Help

Taxonomy classifications: Columns

Software Versions

nf-core/mag Methods Description

Methods

References

Notes:

nf-core/mag Workflow Summary

v1.29

Highlight Samples

Rename Samples

Show / Hide Samples

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences by sample

Adapter Content

Status Checks

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences by sample

Adapter Content

Status Checks

Paired-end alignments

Paired-end alignments

Bin quality

MAG taxonomy